Big global banks are increasingly turning toward Artificial Intelligence (AI) technologies to stay competitive in the digital era. AI has huge benefits, for both banks and their customers. The implications of AI disruption in the financial sector is that the analysis of users’ habits, activities, behavioral characteristics, and financial data products can be customized to meet and anticipate each user’s unique and evolving needs. This makes it viable for each user to have his/her own digital personal financial assistant.
These are the most relevant application areas of AI technology in Banking and Finance:
Personalized Financial Services
Automated financial advisors and planners assist users in taking financial decisions. They monitor events, stock and bond price trends against the user’s financial goals and personal portfolio, and offer recommendations regarding stocks and bonds to buy or sell. These systems are often called “robo advisors” and are progressively offered both by new businesses and established financial service providers.
Digital wallets are billed in most tech circles as the future of real-world payment technologies. With major players like Google, Apple, Paypal and others jumping on the bandwagon and developing their own mobile-first payment technologies, it appears to be a safe bet. Smart wallets – a more evolved version – are on the horizon. Smart wallets monitor and learn users’ habits and needs. They alert and coach users wherever appropriate, to show restraint and to alter their personal finance spending and saving behavior.
The insurance sector is utilizing AI systems that automate the underwriting process and provide more granular information to take better decisions.
Voice Assisted Banking
This technology empowers customers to use banking services with voice commands rather than a touch screen. The natural language technology can process queries to answer questions, find information, and connect users with various banking services. Barclays is currently developing a technology that will enable users to carry out money transfers by talking to a robot computer system. The AI system will be similar to Apple’s iPhone personal assistant, Siri.
Data-driven AI applications for lending decisions
Applications embedded in end user devices, personal robots, and financial institution servers are capable of analyzing massive volumes of information, providing customized financial advice, calculations and forecasts. These applications can also develop financial plans and strategies, and track their progress. This includes research regarding various customized investment opportunities, loans, rates, fees, etc.
Wealth Management for clients
One of the banking areas that have seen a considerable investment in AI is wealth management. Both incumbents and newcomers are realizing that the digital shift happening in the banking space would affect this sector. Industry heavyweights are acquiring tech start-ups with special focus on automatic analysis of large amounts of unstructured data. The purpose is to detect “typical” behavioral patterns. These experts are hoping to build AI engines, which can provide insights on how to best service their high-net-worth clients. By automating large parts of the wealth management process, they would be able to offer personalized, tax-optimized investments to clients, who have far less in investable assets than what would usually qualify for professional wealth management.
As speech processing and natural language processing technologies mature, we are drawing closer to the day, when computers could handle most customer service queries. This would mark an end to waiting in line and hence result in happier customers.
New Management Decision-making
Data-driven management decisions at low cost could lead to a new style of management, where future banking and insurance leaders would ask right questions to machines, rather than to human experts, which would analyze data and come up with recommended decisions that leaders and their subordinates would use and motivate their workforce to execute.
Reducing Fraud and Fighting Crime
Most industries operating on the World Wide Web are susceptible to fraudulent users and the banking industry is no exception. This has led to an arms race between online security providers and fraudsters involved in everything from email scams to credit card frauds. As security providers improve, criminals change their ways. AI tools, which learn and monitor behavioral patterns of users to identify anomalies and warning signs of fraud attempts and occurrences, along with collection of evidence necessary for conviction, are also becoming more commonplace in fighting crime.
The Road Ahead
Digital disruption is a top-of-mind technological issue in the C-suite today. All institutions are striving to determine whether they should ignore, acquire, partner or compete with their new technology-driven competitors. Over $25bn has been poured into Fintech over the past five years, making it the number-one target for venture funding. Overall, an estimated 4,000 firms are challenging banks in every product line in their portfolios — from payments to lending to foreign exchange.
JP Morgan CEO Jamie Dimon told his shareholders about Fintech: “They all want to eat our lunch. Every single one of them is going to try.”
However, some executives believe that digital or AI disruption is a hype that will fade away. Not bankers. Over 90% of bankers project that AI would have a significant impact on the future landscape of banking.
Banks are facing risks from Fintechs. A major explanation behind this is the extent to which Fintechs adopt and leverage AI technologies to beat banks in their own areas. However, the banking industry can always catch up by building internal competencies in areas of AI, data science, and machine learning. Banks can adopt the same AI tools used by Fintechs. However, it would be important to ensure that these intelligent applications are developed in such a way that they provide the desired benefits and that the user could trust the advice and services provided. Another concern for financial institutions is how regulators would respond and supplement guidance on the use of AI.
Interestingly, banks and Fintech firms usually have business interests in common than issues that divide them. Clearly, some Fintech firms will choose to go it alone and some banks will stick to traditional banking products. However, eventually Fintechs will have to make the move from start-ups to being real financial services firms. In the banking world, this effectively means taking on the regulators, becoming proficient in risk management, ensuring data security and building the technology to support these capabilities.
The key to success
The key to success will be partnering well. Thus, the lucky few that can synergize their models with existing institutions with trusted brands, deep pockets, industry expertise and millions of customers will be the ones that will pull ahead of their peers and achieve rapid scale. Hence, banks would prove to be the strategic partners that Fintech firms would need to achieve scale and sustainability. On the other hand, banks have risk and process-focused cultures because their regulators and their business practices demand it. Hence, partnering with Fintech firms would bring in the required agility in adoption and acceptance of technology disruptions.
The best part is, despite any kind of disruption technology or otherwise, the winner will be the consumer who will receive lower prices, more innovative products, and better services in a transformed banking world.
This post is a continuation of A tour through past and present of Artificial Intelligence (AI) by Ritwik Dey.
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